Reversible data hiding techniques have garnered increasing scholarly attention for their capacity to generate high-fidelity watermarked images while enabling lossless restoration of the original images. Pixel value ordering (PVO) stands as a pivotal reversible data embedding approach that adeptly leverages pixel sequencing within an image block to facilitate high-fidelity image embedding and reversible retrieval. The efficacy of PVO-based methodologies hinges on the block size and classification thresholds. Nonetheless, many techniques ascertain the appropriate parameters through exhaustive searches within confined search spaces, demanding significant computational time. This paper introduces a novel pixel value ordering method based on differential evolution (PVO-DE) to address these challenges more effectively. PVO-DE dynamically identifies the optimal or suitable block size and threshold based on distinct images and embedding quantities, thereby optimizing image utilization and reducing time expenditures. Furthermore, a complexity algorithm, fused with the differential evolution algorithm, is proposed to harness the strengths of diverse complexity algorithms and pinpoint the algorithm most conducive to the current image for optimal performance. Results demonstrate a reduction in computational costs for determining the optimal parameter set, alongside enhanced performance of marked images compared to prior approaches.

Side-Channel Attacks (SCAs) now require more side-channel traces for successful execution, which places more stringent requirements on the storage capacity and computational ability of the devices on which SCAs are based. To reduce the storage and computational pressure on the local device where SCAs are performed on collected leakage traces from an attacking device, this paper proposes a secure cloud outsourcing protocol to perform Principal Component Analysis (PCA) dimensionality reduction on the side-channel traces. Secure cloud outsourcing is applied for the computationally intensive matrix multiplication and eigenvalue decomposition of the PCA process. The proposed protocol has been proven to balance privacy, efficiency, and correctness. Through experiments on CW and Grizzly datasets, it shows that 1) Correlation Power Analysis (CPA) with PCA effectively mitigates noise, improving the probability of a successful CPA; 2) Cloud-based PCA significantly reduces the computational complexity of local devices; 3) Template Attacks (TAs) are performed on leakage trace data using cloud-based PCA, client-based PCA, Linear Discriminant Analysis (LDA) and Independent Component Analysis (ICA). The attack results of cloud-based and client-based PCA are basically identical, and they achieve lower Guessing-Entropy (GE) than ICA. Both theoretical analysis and experimental results demonstrate the feasibility and advantages of this protocol.

Deploying Deep Neural Network (DNN) models of varying capabilities to support collaborative inference for User Equipment (UE) tasks is becoming increasingly common in Mobile Edge Computing (MEC) systems. In this paper, we aim to minimize energy consumption in a dynamic Non-Orthogonal Multiple Access (NOMA)-based MEC system assisted by a Simultaneously Transmitting and Reflecting Reconfigurable Intelligent Surface (STAR-RIS) under complex Non-Line-of-Sight (NLoS) conditions, while meeting latency requirements and preserving inference accuracy for UEs. We formulate the problem as a non-convex optimization and address it using a Decomposition-Based Twin Delayed Deep Deterministic Policy Gradient (DB-TD3) approach. The problem is decomposed into two subproblems: 1) computation resource allocation and power optimization, and 2) optimization of the offloading ratio, time fractions allocated for reflection and transmission, phase shift, and transmission time. For the first subproblem, we derive optimal CPU frequencies and transmit power allocation through theoretical analysis. For the second subproblem, the offloading ratio, time fractions allocated for reflection and transmission, phase shift, and transmission time are optimized using the TD3 algorithm. Experimental results demonstrate that the DB-TD3 method significantly improves system efficiency and reduces average energy consumption by 59.3% compared to baseline algorithms. Furthermore, the proposed NOMA with STAR-RIS scheme outperforms other offloading methods, achieving an average energy reduction of 41.3%.

Side-channel attacks (SCAs) serve as a fundamental tool for evaluating the implementation security of cryptographic devices. In real-world acquisition scenarios, however, power consumption traces are frequently degraded by device clock jitter and external noise interference, resulting in pronounced temporal misalignment and signal distortion. As a result, the efficiency and stability of attack convergence are seriously restricted. To address this, we propose a correlation power analysis (CPA) framework that integrates successive variational mode decomposition (SVMD) and peak-anchor-based alignment (PA-CPA). Firstly, the method uses SVMD to adaptively decompose and reconstruct the original power consumption traces, effectively suppressing random interference and preserving leakage-relevant features. A robust anchor point sequence is then constructed and global linear resampling and local dynamic time warping (DTW) are combined to realise the segmental fine alignment of the power consumption traces. This improves feature synchronisation and alignment accuracy. Experimental results demonstrate that the proposed method achieves higher attack success rates and faster convergence.

ABSTRACT Epilepsy is a complex chronic brain disorder that can be identified by observing brain signals. In general, the electroencephalogram (EEG) can be used to detect these brain signals. In order to produce a high‐quality model, data from numerous patients can be gathered on a central server. However, sending the patient's raw data to the central computer may lead to privacy leakage. To address this problem, this work uses federated learning and differential privacy to train the model jointly. Furthermore, the epilepsy data is unbalanced as seizure only happens for a minority of time in one day, which influences the performance of the model. Thus, this work also uses label‐distribution‐aware‐margin (LDAM) loss to solve this issue. This work is evaluated in intracranial EEG datasets, which consist of two dogs' EEG records. The global model trained jointly with LDAM loss can achieve an accuracy of 96.95%, a sensitivity of 78.9%, a specificity of 96.145%, an F1 score of 70.435%, and a geometric mean of 87.785%. Compared with the other works, the accuracy has improved by about ˜9.31%, while the specificity and the geometric mean have also improved by about ˜10.75% and ˜1.8%, respectively.

Abstract Chaotic systems play an indispensable role in the fields of cryptography and information security. Sine-Transform-Based Chaotic System (STBCS) can address the shortcomings of low complexity and limited chaotic behaviour of classical chaos systems. In this paper, a compact hardware STBCS is proposed and developed on the FPGA device by using the Stochastic Computation (SC) technique. The traditional arithmetic operations are replaced by the SC and finite state machines design. The structure of STBCS is optimised, where the disturbance method is employed to improve the chaotic behaviours and also taking the SC method into account for implementation. The hardware performance of the proposed design is verified via various tests of the chaotic system and corresponding random number generator. Experimental results show that the utilisation of the hardware resources is reduced especially the DSP components compared to the traditional design methods. This provides an efficient design for the random generator of the alternative cryptosystems.

Random Boolean Network (RBN) is a type of regulatory network in which the nodes have Boolean values representing their states. The robustness of RBNs against perturbations is a crucial characteristic, and there has been a growing interest in enhancing the network's robustness. In this study, a biologically inspired epigenetic regulation method is proposed to enhance the robustness of the RBNs. A frequency encoding method based on pulse counting is employed to encode the node states within a sliding time window, thereby improving the form of epigenetic regulation. To verify the performance of this method, an antifragility indicator is adopted to measure the robustness of RBNs and yeast cell networks at different scales. The experimental results demonstrate that the networks with epigenetic regulation exhibit excellent robustness, even in the presence of large-scale networks and severe perturbations. This approach provides a new perspective and idea for designing robust RBNs and discrete networks.

Federated Learning (FL) represents a promising distributed learning paradigm that enables model training without centralizing users' sensitive data. However, FL faces several practical challenges, such as communication overhead, convergence rates, robustness, and overall performance efficacy. A paramount research objective within FL is to achieve rapid convergence without compromising privacy. Regrettably, the existing body of research on this subject is scant, with most studies concentrating solely on privacy safeguards or the optimization of adaptive learning algorithms. To address this gap, this work introduces an adaptive gradient descent algorithm underpinned by Differential Privacy (DP). The approach synergizes the TensorFlow federated learning framework with a suite of techniques, including relaxed DP, subsampling for privacy amplification, and the shuffle model, to expedite convergence while bolstering privacy protection. The empirical outcomes on the MNIST dataset show that the FAdagrad algorithm accomplishes a test accuracy of 82.29% within just 12 iterations, all the while maintaining a tighter privacy control (with a privacy budget of 0.5). In comparison to the DP-FL, Simple, and Topk algorithms that employ conventional differential privacy techniques and attain peak test accuracies of 52.17%, 71.09%, and 72.67% respectively under analogous conditions, the FAdagrad algorithm introduced in this paper markedly improves the key performance indicators of the model.

Deep Neural Networks (DNNs) have made significant progress in the field of artificial intelligence, and machine learning as a service has become a service that can be profitable. Intellectual property protection for DNNs has become an important issue. These DNNs models may be misappropriated by third-party, thus causing financial losses to the model owners. In the existing white-box and black-box approaches, protected models cannot be actively verified for ownership, and they can only be verified by sending a specific sample after the model has been stolen. In this work, a black-box DNNs watermark protection framework is proposed for protecting intellectual property of image classification models, which embeds watermarks in DNNs models. The proposed framework is applicable to models for image classification, and only images containing secret watermarks can be correctly recognized by the model. Besides, the proposed framework can correctly identify the ownership of the model, resist pruning operations of the model, and resist malicious tampering of the input image. Experimental results show that the watermarking framework is robust and effective.

Pointwise order dependencies (PODs) are dependencies that specify ordering semantics on attributes of tuples. POD discovery refers to the process of identifying the set Σ of valid and minimal PODs on a given data set D. In practice D is typically large and keeps changing, and it is prohibitively expensive to compute Σ from scratch every time. In this paper, we make a first effort to study the incremental POD discovery problem, aiming at computing changes ΔΣ to Σ such that Σ ⊕ ΔΣ is the set of valid and minimal PODs on D with a set Δ D of tuple insertion updates. (1) We first propose a novel indexing technique for inputs Σ and D. We give algorithms to build and choose indexes for Σ and D , and to update indexes in response to Δ D. We show that POD violations w.r.t. Σ incurred by Δ D can be efficiently identified by leveraging the proposed indexes, with a cost dependent on log (| D |). (2) We then present an effective algorithm for computing ΔΣ, based on Σ and identified violations caused by Δ D. The PODs in Σ that become invalid on D + Δ D are efficiently detected with the proposed indexes, and further new valid PODs on D + Δ D are identified by refining those invalid PODs in Σ on D + Δ D. (3) Finally, using both real-life and synthetic datasets, we experimentally show that our approach outperforms the batch approach that computes from scratch, up to orders of magnitude.

MicroRNAs (miRNAs) are crucial regulators of gene expression at post-transcriptional level. Understanding origin and evolution of miRNAs will improve the current available algorithm for the prediction of novel miRNAs and their functions. Transposable elements (TEs) provide a natural mechanism for the origin of new miRNAs. In the paper, 2,583 miRNAs derived from TEs (MDTEs) were collected to construct a database named MDTE database (MDTE DB) for storing, searching, and analyzing MDTEs. The database provides a convenient source for studying the origin and evolution of miRNAs. Database URL:http://bioinf.njnu.edu.cn/MDTE/MDTE.php.

Abstract Motivation: Gene regulatory networks (GRNs) affect numerous cellular processes and every process of life, and abnormalities of GRN lead to breast cancer. Transcription factors (TFs) and microRNAs (miRNAs) are two of the best-studied gene regulatory mechanisms. However, the architecture and feature of GRNs by TFs and miRNAs in breast cancer and its subtypes were unknown. In this study, we investigated the GRNs by TFs and miRNAs with emphasis on breast cancer classifier genes at system level. Contact: [email protected] or [email protected] Supplementary information: Supplementary data are available at Bioinformatics online.